中药生产过程质量控制关键技术研究进展

中医药发展已上升到国家战略层面,在医药行业贯彻实施"中国制造2025"战略的新形势下,中药生产过程质量控制是中药工业需要加快突破的关键领域之一。对中药生产过程质量控制领域在工艺设计、分析检测、过程建模、制造装备等方面的关键共性问题进行解析,综述了中药生产过程质量控制体系中工艺过程理解、生产过程实时分析方法开发、过程控制策略建立3个方面的研究进展;并结合企业研究实践,介绍了质量源于设计(quality by design,Qb D)、过程分析技术(process analytical technology,PAT)、实验设计(design of experiment,DOE)、多变量统计分析等关键技术在上述3个研究方向中的应用进展,分析了实际工业应用的难点问题并对其应用前景进行展望,旨在为中药企业应用和提升生产过程质量控制技术提供参考。     

Hypocholesterolaemic mechanism of bitter melon aqueous extracts via inhibition of pancreatic cholesterol esterase and reduction of cholesterol micellar solubility

This study investigated the hypocholesterolaemic effects of bitter melon aqueous extracts (BMAE) in vitro, the inhibitory effects of BMAE on pancreatic cholesterol esterase (CEase) and incorporation of cholesterol into micelles were investigated. BMAE decreased the in vitro micellar solubility of cholesterol in a dose-dependent manner. The conformation of CEase was investigated by means of circular dichroism (CD) and fluorescence. The result revealed the decrease of α-helix contents, increase of β-sheet and exposure of aromatic amino acid residuals. The incorporation of cholesterol into micelles was inhibited by BMAE. A complex was observed by transmission electron microscopy (TEM), which indicated interaction between cholesterol and BMAE. The result revealed that BMAE can play a role in decreased intestinal cholesterol absorption via inhibition of CEase, and of micelle formation.     

Exact confidence intervals for the average causal effect on a binary outcome

Based on the physical randomization of completely randomized experiments, in a recent article in Statistics in Medicine, Rigdon and Hudgens propose two approaches to obtaining exact confidence intervals for the average causal effect on a binary outcome. They construct the first confidence interval by combining, with the Bonferroni adjustment, the prediction sets for treatment effects among treatment and control groups, and the second one by inverting a series of randomization tests. With sample size n, their second approach requires performing O(n⁴)randomization tests. We demonstrate that the physical randomization also justifies other ways to constructing exact confidence intervals that are more computationally efficient. By exploiting recent advances in hypergeometric confidence intervals and the stochastic order information of randomization tests, we propose approaches that either do not need to invoke Monte Carlo or require performing at most O(n²)randomization tests. We provide technical details and R code in the Supporting Information . Copyright © 2016 John Wiley & Sons, Ltd.